A0585
Title: Design for small MPV experiments with high-dimensional model terms
Authors: Chang-Yun Lin - National Chung Hsing University (Taiwan) [presenting]
Abstract: The design construction and model selection are considered for mixture-process variable experiments where the number of variables is large. For such experiments, the generalized least squares estimates cannot be obtained, and hence, it will be difficult to identify the important model terms. To overcome these problems, the generalized Bayesian-D criterion is employed to choose the optimal design and apply the Bayesian analysis method to select the best model. Two algorithms are developed to implement the proposed methods. A fish-patty experiment demonstrates how the Bayesian approach can be applied to a real experiment. Simulation studies show that the proposed method has a high power to identify important terms and well control the type I error.
